Electric vehicle polymer state transition and frequency control parameter self-tuning method

Abstract

The invention belongs to the field of electrical load management, and particularly relates to an electric vehicle polymer state transition and frequency control parameter self-tuning method. The method comprises the following steps: firstly, constructing an electric vehicle polymer state transition and frequency control model by using a state space method; secondly, proposing a frequency coordination control strategy of an electric vehicle polymer for the situation that the power regulating variables of a traditional generator set and an electric vehicle cluster are controlled by a dispatching center; and finally, an electric vehicle cluster participation load frequency control model is built, and an electric vehicle polymer participation frequency control parameter self-tuning method is provided. According to the method, the system frequency response participation capability of the electric vehicle polymer is improved, and the frequency control parameter setting efficiency of the electric vehicle polymer is improved.

Description

Technical Field

[0001] The invention belongs to the field of power load management, in particular to a method for self-tuning state transfer and frequency control parameters of an electric vehicle polymer. Background Art

[0002] User-side load frequency control refers to the use of the demand response capability of controllable loads to control system frequency deviations. Before implementing regulation, the power company signs a contract with the controllable load user; when the system has a frequency deviation, incentives or coercive means are used to guide or control the power consumption behavior of the controllable load. Specifically, the power of the controllable load is generally appropriately reduced during peak power consumption, and the power of the controllable load is increased during low power consumption, thereby compensating for the unbalanced power of the system.

[0003] However, the frequency response of electric vehicle polymer-based systems currently suffers from problems such as complex parameter setting methods and poor frequency control effects. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for self-tuning of electric vehicle polymer state transfer and frequency control parameters, thereby improving the frequency response capability of electric vehicle polymers participating in the system and improving the tuning efficiency of electric vehicle polymer frequency control parameters.

[0005] The control parameter self-tuning method refers to a method of automatically adjusting the controller parameters by utilizing the dynamic characteristics of the system. The application of parameter self-tuning technology in the frequency control of electric vehicle aggregates will greatly enhance the ability of electric vehicle aggregates to participate in the system frequency response.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] The present invention proposes a method for self-tuning state transfer and frequency control parameters of an electric vehicle aggregate. Firstly, a state space method is used to construct a state transfer and frequency control model of an electric vehicle aggregate. Secondly, for the situation where the power regulation quantities of traditional generator sets and electric vehicle clusters are both controlled by a dispatching center, a frequency coordination control strategy for electric vehicle aggregates is proposed. Finally, a model for electric vehicle clusters to participate in load frequency control is built, and a method for self-tuning frequency control parameters of electric vehicle aggregates is proposed.

[0008] The steps of the electric vehicle polymer participating frequency control parameter self-tuning method are as follows:

[0009] Step 1: Using the dynamic characteristics of electric vehicle polymers, determine the category of the standard model of electric vehicle polymers. If it is a first-order plus time delay model (FOPDT model), proceed to step 2; if it is an underdamped second-order plus time delay model (underdampedSOPDT model), proceed to step 3;

[0010] Step 2: According to the dynamic characteristics, determine the parameters K, T, τ in the model, and proceed to step 4, where K is the FOPDT model amplification factor, T is the FOPDT model time constant, and τ is the FOPDT model delay time;

[0011] Step 3: According to the dynamic characteristics, determine the parameters K, T, τ, ξ in the model, and go to step 5; wherein K is the underdamped SOPDT model amplification factor, T is the underdamped SOPDT model time constant, τ is the underdamped SOPDT model delay time, and ξ is the underdamped SOPDT model damping coefficient;

[0012] Step 4: Substitute the model parameters into (40) to obtain the self-tuning PID controller expression and proceed to step 6:

[0013]

[0014] in

[0015] Step 5: Substitute the model parameters into (41) to obtain the self-tuning PID controller expression and proceed to step 6:

[0016]

[0017] in

[0018] Step 6: Substitute the electric vehicle aggregate frequency controller into the electric vehicle cluster load frequency control model to achieve frequency self-tuning control.

[0019] The FOPDT model is expressed as:

[0020]

[0021] The UnderdampedSOPDT model is expressed as:

[0022]

[0023] The advantages and positive effects of the present invention are:

[0024] The method of the present invention improves the frequency response capability of the electric vehicle polymer participating system and improves the setting efficiency of the frequency control parameters of the electric vehicle polymer. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of electric vehicles participating in power system frequency control;

[0026] Figure 2 It is a schematic diagram of coordinated frequency control strategy;

[0027] Figure 3 This is the block diagram of the frequency coordination control system;

[0028] Figure 4 It is the model of electric vehicle load frequency controller;

[0029] Figure 5 This is the flow chart of self-tuning of state transfer and frequency control parameters of electric vehicle aggregate. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited thereto.

[0031] The load frequency controller of electric vehicle cluster mainly consists of five parts: electric vehicle monomer, electric vehicle cluster, electric vehicle load frequency control center, communication link between the three and control process. Figure 1 As shown in the dotted part above, it can be seen that each electric vehicle belongs to an electric vehicle cluster, and each electric vehicle cluster is controlled by the electric vehicle cluster load frequency control center.

[0032] 1. Construction of electric vehicle aggregate state transfer and frequency control model

[0033] The electric vehicle cluster set is recorded as:

[0034] Ω A ={A1,A2,…,A i ,…,A N-1 ,A N}(1)

[0035] Where: N is the number of electric vehicle clusters; A i ∈Ω A , i=1,2,…,N-1,N is the i-th electric vehicle cluster, and there is

[0036]

[0037] Where: n i , i=1,2,...,N-1, N is the number of electric vehicles in cluster i; E i,j ∈Α i For cluster A i The jth electric car in the EV Ω A The total number of electric vehicles in the

[0038] The electric vehicle Ei,j and Cluster A i The status information is recorded as s i,j and S i ,Right now:

[0039]

[0040] Where: s i,j For electric vehicles i,j The battery capacity, initial SOC, real-time SOC status, and electric vehicle charging mode, such as idle state, grid-to-vehicle (G2V) state, or bidirectional charging (V2G) state.

[0041] Electric Vehicle i,j The controllable power P i,j for:

[0042]

[0043] Where: For electric vehicles i,j The controllable power output, expressed as E i,j Additional injection into the grid Size of power; For electric vehicles i,j The controllable power input, expressed as E i,j More power consumption from the grid Size of power.

[0044] Further, we can get the electric vehicle cluster A i The controllable power P Ai for:

[0045]

[0046] The total controllable power P is obtained by summarizing the electric vehicle load frequency control center EV for:

[0047]

[0048] Where: and are the total injectable power and the total consumable power of the electric vehicle cluster, respectively.

[0049] like Figure 1 As shown by the blue line in the middle, this channel is used to estimate the total controllable power P of the electric vehicle cluster EV First, electric vehicle cluster A i Collect the status information of electric vehicles under its jurisdiction in real time i,j , evaluate the controllable power P of a single electric vehicle i,j, and calculate the controllable power P of the cluster Ai . Further, the controllable power P of each cluster Ai , i=1,2,...,N-1,N, and upload it to the electric vehicle load frequency control center to finally obtain the total controllable power P of the electric vehicle EV , and upload it to the system's dispatch center.

[0050] like Figure 1 As shown by the black line in the middle, this channel is used to control the electric vehicle so that it can respond to the control signal sent by the dispatch center. At the electric vehicle load frequency control center, the control system decomposes the control signal so that it can be distributed to each cluster:

[0051]

[0052] Further, at the i-th electric vehicle cluster, the cluster response control information Decompose again to get a single electric car E i,j The response signal

[0053]

[0054] Finally, each electric vehicle receives the control signal from the control center and changes its power consumption state accordingly. The actual power value of the electric vehicle cluster is Then the total actual response power of the electric vehicle ΔP EV for

[0055]

[0056] In the process of bidirectional transmission of acquisition / control information, electric vehicles E i,j The controllable power P i,j may change; at the same time, the actual power response of the electric vehicle cluster ΔP EV Demand signals sent by the system control center There may also be some deviations.

[0057] 2. Frequency coordination control strategy for electric vehicle aggregates

[0058] Figure 2 The frequency coordination control principle of the generator set and the electric vehicle cluster is given. T is the total unbalanced power of the power system:

[0059]

[0060] Where: and They are the total power generation of various generators and the total load power. The horizontal axis Δf is the system frequency deviation:

[0061] Δf=ff nominal (18)

[0062] Where: f and f nominal They are the actual system frequency and the rated frequency respectively.

[0063] When the frequency deviation Δf exceeds its upper limit Δf U (as shown in point A in the figure) or the lower limit Δf L (as shown in the figure at point B), the total power shortage ΔP R It can be expressed as

[0064]

[0065] Where: and Respectively represent the frequency deviation Δf as Δf U and Δf L Unbalanced power at the time.

[0066] Compared with the power control characteristics of generator sets, the charging / discharging process of electric vehicles is short, so in this paper, electric vehicles have a higher priority in load frequency control than generator sets. After the frequency deviation occurs in the system, the controllable power of electric vehicles is first used to compensate for the unbalanced power. The specific formula is as follows:

[0067] Δf>Δf U

[0068]

[0069] Δf<Δf L

[0070]

[0071] It can be seen from equations (19)-(23) that when the frequency deviation Δf>Δf U When the power response demand of electric vehicles Power response requirements of generator sets It means that the system needs to increase the load power consumption and the generator set reduces the power generation; when the frequency deviation Δf<Δf L When the power response demand of electric vehicles Power response requirements of generator sets It means that the system needs to reduce the load and increase the power generation of the generator sets.

[0072] Figure 3The block diagram of the frequency coordination control system is given: First, the system dispatching center calculates the control signals for the generator set and the electric vehicle cluster according to the coordination control strategy of equations (19)-(23). and Then, the generator and electric vehicle cluster respond to the corresponding control quantity; finally, the response power ΔP m and ΔP EV Act on the power system to eliminate unbalanced power.

[0073] 3. Electric vehicle aggregate load frequency control model

[0074] After receiving the control signal from the system dispatch center, if the electric vehicle is required to reduce its power consumption, it will stop charging or even supply power to the system, that is:

[0075]

[0076] Where: and Electric vehicle monomer E i,j The discharge coefficient and discharge time constant.

[0077] When an electric vehicle cell receives a control signal to increase power consumption, it will increase the power it uses, namely:

[0078]

[0079] Where: and Electric vehicle monomer E i,j The charging coefficient and charging time constant. It can be seen that the charging and discharging coefficient of electric vehicle monomer can be expressed as:

[0080]

[0081] Electric vehicle monomer E i,j The charge and discharge coefficient is related to E i,j The SOC level is related to

[60] ,

[69] , as shown below:

[0082] 1) When hour,

[0083]

[0084] 2) When hour,

[0085]

[0086] 3) When hour,

[0087]

[0088] 4) When hour,

[0089]

[0090] Where: SOC i,j , Electric vehicle monomer E i,j The real-time SOC, initial SOC, maximum allowed SOC and minimum SOC values.

[0091] The electric vehicle load frequency control cluster model used in this paper is shown in Figure 4 The cluster model is derived from the electric vehicle single model. To simplify the discussion, it is assumed that the charging and discharging time constants of electric vehicles in the same cluster are the same, which is T Ai , i=1,2,...,N-1,N. K Ai For electric vehicle cluster A i The charge and discharge coefficient of cluster A i The expected expression of the charging coefficient of all electric vehicle monomers in is:

[0092]

[0093] Where: For electric vehicle monomer E i,j The real-time SOC distribution function of Cluster A i The number of electric vehicles in is n i (i=1,2,...,N-1,N) as the control gain. In the electric vehicle load frequency control center, the droop coefficient R EV Indicates the equivalent gain of electric vehicle power to frequency; K P and K I is the PI controller parameter used to adjust the electric vehicle cluster A i It can be seen that the output power of the electric vehicle cluster is affected by the local frequency deviation and the coordinated control signal of the system dispatching center. joint control.

[0094] Taking all the above into consideration, electric vehicle cluster A i The power can be expressed as:

[0095]

[0096] Where: i=1,2,...,N-1,N.

[0097] From the previous analysis, we can know that in the load frequency control with the participation of electric vehicles, there is data and signal transmission between electric vehicle monomers, clusters, electric vehicle load frequency control centers and system dispatching centers. Traditional dedicated and closed communication networks have high investment and operating costs, and are often difficult to adopt when there are many types of adjustable loads, dispersed geographical locations, and large numbers; and communication networks based on heterogeneous communication technology and with open structures can well meet the requirements of various types of loads participating in power system auxiliary services. However, with the introduction of heterogeneous communication means, especially low-cost heterogeneous communication means, there are often large delays, which inevitably cause communication delays in the system control link. The existence of delays will have an adverse effect on the controllable loads participating in system auxiliary services, especially the system frequency regulation effect. In order to study this impact, it is necessary to establish a time-delay model of frequency deviation:

[0098]

[0099] Where: ΔP T is the total unbalanced power in the system, caused by intermittent renewable energy and other uncontrollable active loads in the system; d i (t) is the electric vehicle cluster response power delay caused by communication delay, and satisfies

[0100] 0≤d i (t)≤τ i ,i=1,2,…,N(36)

[0101]

[0102] Where: τ i and μ i are the upper limit and changing rate of the time-varying lag, respectively.

[0103] 4. Frequency control parameter self-tuning method for electric vehicle polymer

[0104] Generally, a self-balanced and stable system can be roughly divided into a first-order pure damping delay model (FOPDT) without oscillation and a second-order underdamped SOPDT with oscillation.

[0105] FOPDT model:

[0106]

[0107] UnderdampedSOPDT System:

[0108]

[0109] Step 1: Using the dynamic characteristics of electric vehicle polymers, determine the standard model category of electric vehicle polymers. If it is a FOPDT model, proceed to step 2; if it is an underdampedSOPDT model, proceed to step 3.

[0110] Step 2: According to the dynamic characteristics, determine the parameters K, T, τ in the model and go to step 4.

[0111] Step 3: According to the dynamic characteristics, determine the parameters K, T, τ, ξ in the model and go to step 5.

[0112] Step 4: Substitute the model parameters into (40) to obtain the self-tuning PID controller expression and proceed to step 6:

[0113]

[0114] in

[0115] Step 5: Substitute the model parameters into (41) to obtain the self-tuning PID controller expression and proceed to step 6:

[0116]

[0117] in τ R =2Tξ, τ f =0.05τ D .

[0118] Step 6: Substituting the EV Polymer Frequency Controller into Figure 4 The controller model shown implements frequency self-tuning control.

[0119] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept, which all belong to the protection scope of the present invention.

Claims

1. A method for self-tuning state transfer and frequency control parameters of electric vehicle polymer, characterized in that: Firstly, the state space method is used to construct the state transfer and frequency control model of electric vehicle aggregates. Secondly, for the situation where the power regulation of traditional generator sets and electric vehicle clusters are controlled by the dispatching center, a frequency coordination control strategy for electric vehicle aggregates is proposed. Finally, a model of electric vehicle clusters participating in load frequency control is built, and a method for self-tuning parameters of electric vehicle aggregates participating in frequency control is proposed. The steps of the electric vehicle polymer participating frequency control parameter self-tuning method are as follows: Step 1: Using the dynamic characteristics of electric vehicle aggregates, determine the category of the standard model of electric vehicle aggregates. If it is a first-order plus time-delay model, proceed to step 2; if it is an underdamped second-order plus time-delay model, proceed to step 3; Step 2: According to the dynamic characteristics, determine the parameters K, T, τ in the model, and proceed to step 4, where K is the FOPDT model amplification factor, T is the FOPDT model time constant, and τ is the FOPDT model delay time; Step 3: According to the dynamic characteristics, determine the parameters K, T, τ, ξ in the model, and go to step 5, where K is the underdamped SOPDT model amplification factor, T is the underdamped SOPDT model time constant, τ is the underdamped SOPDT model delay time, and ξ is the underdamped SOPDT model damping coefficient; Step 4: Substitute the model parameters into (40) to obtain the self-tuning PID controller expression and proceed to step 6: in Step 5: Substitute the model parameters into (41) to obtain the self-tuning PID controller expression and proceed to step 6: in Step 6: Substitute the electric vehicle aggregate frequency controller into the electric vehicle cluster load frequency control model to achieve frequency self-tuning control.

2. The electric vehicle polymer state transfer and frequency control parameter self-tuning method according to claim 1, characterized in that: The first-order plus delay model is expressed as: Among them, K is the FOPDT model amplification factor, T is the FOPDT model time constant, and τ is the FOPDT model delay time.

3. The electric vehicle polymer state transfer and frequency control parameter self-tuning method according to claim 1, characterized in that: The underdamped second-order plus time-delay model is expressed as: Among them, K is the underdamped SOPDT model amplification factor, T is the underdamped SOPDT model time constant, τ is the underdamped SOPDT model delay time, and ξ is the underdamped SOPDT model damping coefficient.

4. The electric vehicle polymer state transfer and frequency control parameter self-tuning method according to claim 1, characterized in that: The frequency coordination control strategy of the electric vehicle aggregate includes: ΔP T is the total unbalanced power of the power system: Where: and They are the total power generation of various generators and the total power of loads; The horizontal axis Δf is the system frequency deviation: Δf=f-f nominal Where: f and f nominal They are the actual system frequency and rated frequency respectively; When the frequency deviation Δf exceeds its upper limit Δf U Or lower limit Δf L After that, the total power shortage ΔP R It is expressed as: Where: and Respectively represent the frequency deviation Δf as Δf U and Δf L Unbalanced power at When the system has frequency deviation, the controllable power of the electric vehicle is first used to compensate for the unbalanced power, as shown in the following formula: Δf>Δf U Δf<Δf L When the frequency deviation Δf>Δf U When the power response demand of electric vehicles Power response requirements of generator sets It means that the system needs to increase the load power consumption and the generator set reduces the power generation; when the frequency deviation Δf<Δf L When the power response demand of electric vehicles Power response requirements of generator sets It means that the system needs to reduce the load and increase the power generation of the generator sets.

5. The electric vehicle polymer state transfer and frequency control parameter self-tuning method according to claim 1, characterized in that: The electric vehicle cluster participating in the load frequency control model includes the following formula: The power of the electric vehicle cluster Ai is expressed as: Where: i = 1, 2, ..., N-1, N; K Ai is the charge and discharge coefficient of the electric vehicle cluster Ai, which is expressed by the expected charging coefficient of all electric vehicles in the cluster Ai. For electric vehicle monomer E i,j The distribution function of the real-time SOC, the number of electric vehicles in the cluster Ai is n i (i=1,2,...,N-1,N) as the control gain, in the electric vehicle load frequency control center, the droop coefficient R EV Indicates the equivalent gain of electric vehicle power to frequency; K P and K I is the PI controller parameter used to adjust the input signal of the electric vehicle cluster Ai, the output power of the electric vehicle cluster, and the local frequency deviation and the coordinated control signal of the system dispatching center joint control; Where: ΔP T is the total unbalanced power in the system, caused by intermittent renewable energy and other uncontrollable active loads in the system; d i (t) is the electric vehicle cluster response power delay caused by communication delay, and satisfies 0≤d i (t)≤τ i ,i=1,2,…,N Where: τ i and μ i are the upper limit and changing rate of the time-varying lag respectively.

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